Virus–Host Protein–Protein Interactions between Human Papillomavirus 16 E6 A1 and D2/D3 Sub-Lineages: Variances and Similarities
Bibliographic record
Abstract
High-risk strains of human papillomavirus are causative agents for cervical and other mucosal cancers, with type 16 being the most frequent. Compared to the European Prototype (EP; A1), the Asian-American (AA; D2/D3) sub-lineage seems to have increased abilities to promote carcinogenesis. Here, we studied protein-protein interactions (PPIs) between host proteins and sub-lineages of the key transforming E6 protein. We transduced human keratinocyte with EP or AA E6 genes and co-immunoprecipitated E6 proteins along with interacting cellular proteins to detect virus-host binding partners. AAE6 and EPE6 may have unique PPIs with host cellular proteins, conferring gain or loss of function and resulting in varied abilities to promote carcinogenesis. Using liquid chromatography-mass spectrometry and stringent interactor selection criteria based on the number of peptides, we identified 25 candidates: 6 unique to AAE6 and EPE6, along with 13 E6 targets common to both. A novel approach based on pathway selection discovered 171 target proteins: 90 unique AAE6 and 61 unique EPE6 along with 20 common E6 targets. Interpretations were made using databases, such as UniProt, BioGRID, and Reactome. Detected E6 targets were differentially implicated in important hallmarks of cancer: deregulating Notch signaling, energetics and hypoxia, DNA replication and repair, and immune response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".